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Severe DILI was defined as Scheuer inflammation grade plus fibrosis stage \u2265 5 (G\u2009+\u2009S\u2009\u2265\u20095). Dual elastography-derived activity index (A index) and fibrosis index (F index) correlated with pathological inflammation (G0\u20134) and fibrosis (S0\u20134) stages. The dataset was stratified and split 7:3 into training and test sets. LASSO regression was applied for feature selection. Eight ML models were constructed and compared, optimized using 5-fold cross-validation and Bayesian methods. Performance was evaluated by area under the curve (AUC), sensitivity, and specificity. SHapley Additive exPlanations (SHAP) were used to interpret the models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 305 participants were included (median age 49 years, IQR 40\u201356; 98 male), comprising 55 with severe DILI and 250 without. A and F indices increased with inflammation grade and fibrosis stage, respectively (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01). Combining clinical and dual elastography features with serum biomarkers, the optimized regularized regression model performed best in the test set (AUC 0.862 [95% CI: 0.776\u20130.947]; sensitivity 81.2%; specificity 74.7%). SHAP analysis identified the dual elastography indices collectively as the dominant predictors. An online risk calculator was developed from this model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed an explainable, high-performing and dual elastography-based ML model to predict severe DILI, facilitating risk stratification and preliminary management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Severe drug-induced liver injury (DILI) leads to a poor prognosis, yet early noninvasive identification remains challenging due to non-specific serum markers and invasive biopsy.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>The regularized regression model integrating dual elastography, clinical features and serum biomarkers, non-invasively and robustly predicted severe histologic injury in DILI.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>This dual elastography-based machine learning model offers a noninvasive solution for the early identification of DILI patients with severe tissue injury, minimizing unnecessary biopsy and improving prognosis. Clinicians can utilize the online tool for real-time risk stratification at:<\/jats:italic>\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/wznng666.shinyapps.io\/RR55555\/\" ext-link-type=\"uri\">https:\/\/wznng666.shinyapps.io\/RR55555\/<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12644-y","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T07:33:11Z","timestamp":1780903991000},"page":"7609-7620","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Noninvasive prediction of severe histopathology in drug-induced liver injury using a dual elastography-based machine learning model"],"prefix":"10.1007","volume":"36","author":[{"given":"Luping","family":"Qiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyun","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fankun","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangwen","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiabao","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9998-0904","authenticated-orcid":false,"given":"Hong","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"key":"12644_CR1","doi-asserted-by":"crossref","unstructured":"European Association for the Study of the Liver (2019) EASL clinical practice guidelines: drug-induced liver injury. 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